Yobi
Machine Learning Engineer - Core Signals
Remote (US + Canada Only)
Sponsorship not specified$180k-$275kDetected 734 days ago
Full-Stack DevelopmentGitDatabricksCI/CDMachine LearningSparkAirflowLLMsResearchCollaboration
About the role
- As an MLE on the Core Signals team, you will primarily be focused on (1) the representation learning surrounding our fundamental user-behavioral modeling problems and (2) using those core models to power new and existing products.
- This role involves a lot of collaboration with the Product org and Applications team to realize these R&D gains - but is ultimately a "full stack" ML role.
- Day to day responsibilities include data processing, model training, deployment, and evaluation.
Responsibilities
- Unlike traditional LLM companies, Yobi builds foundation models of human behavior grounded in real-world actions such as purchases and store visits.
- Our private-by-design modeling enables state-of-the-art personalization and decisioning for leading brands and agencies while protecting privacy, safety, and ethics.
- At our core, Yobi is building the behavioral intelligence layer for any system that makes a personalization decision.
- Engineers here own major surface areas, build 0→1 systems in large-scale data and model infrastructure, and help define how Behavioral AI scales ethically and effectively.
- This includes things such as data orchestration, build systems, and experiment tracking.
- Tell us how you can help drive our products forward, even if you don't feel like you are a perfect fit for some of the listings.
Requirements
- Previous publication experience is not required.
- Although we use a combination of open source products like Airflow, Bazel, Github CI/CD, and Spark, prior experience with these specific solutions is not needed.
- Good product sense - you have opinions on what we should and shouldn't be doing both in chasing product-market fit and on the implementation side.
Compensation
- $180,000-$275,000
- A reasonable estimate of the current base salary range at the time of posting is below.
- Base salary does not include other forms of compensation or benefits.
- Competitive Base Salary
Benefits
- Meaningful equity & financial upside - a real % of the company
- Annual bonus target based on personal and company performance
- Health, Dental, Vision available
- Unlimited PTO - we care about impact, not tracking days you're out
- You understand enough about machine learning to be able to apply it to novel problems and suggest improvements to our current setup, or even radical new approaches.
- You've worked on and can speak to at least some of: representation learning, embeddings drift, CTR modeling, sequence modeling, etc., preferably on "big data" in an industrial setting.
- Base salary does not include other forms of compensation or benefits.
- Yobi is a rapidly growing Behavioral AI company on a mission to ethically democratize the benefits of data and AI.
Company info
- Well-funded with 5+ years of runway. At the same time, we are scaling revenue quickly and project to be breakeven in 2026.
- Partnerships with Microsoft and Databricks
- Fully remote or hybrid from several hubs (SF Bay Area, Seattle, NYC)
- World-class team of Machine Learning experts who worked on cutting edge infra and recommender systems @ Amazon, Uber, Twitter, Meta, etc.
- Product and Go-To-Market teams who have taken ideas from concept to 9 figure revenue streams
- If our mission and work resonates with you, we encourage you to apply.
- Today, we are focused on bringing the performance of closed-web user acquisition to the open web and connected TV, giving brands walled-garden results without the walls.
- A good amount of "wearing your Product hat" is expected, as well as the ability to flex into some other functions as needed - we are a quickly growing startup after all.
- However, a good part of your day to day will involve interacting with these systems, so you should be comfortable with getting your hands dirty.
This listing is sourced directly from Yobi's careers page and normalized into a canonical job model.